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Prepare input file for Diels-Alder reaction

DeepCV's input file must be in a JSON file format (dictionary-like). An example of DAENN input in inputs/ folder shows the configuration for training a model using DAENN with five hidden layers. The first three hidden layers contain two encoded layers and one latent encoded layer (middle layer). The rest layers are two decoded layers for reconstruction. On the other hand, the size of two hidden layers that are opposite of each other, e.g., input and output layers (the 1st and 5th hidden layers) must be the same.



Key Definition Value
name Project name String
neural_network Type of neural network daenn, gan


Key Definition Value
primary A list of dataset files for primary loss function String
secondary A list of dataset files for secondary loss function String
split Split dataset Logical: true, false
split_ratio Ratio for splitting (for training set) Integer: 0.8
shuffle Shuffle the data points Logical: true, false
normalize_scale Normalization scaling value Float: 0.0
max_scale Maximum scaling value Integer: 1


Key Definition Value
optimizer Optimizer Adadelta, Adagrad, Adam, Adamax, Ftrl, Nadam, RMSprop, SGD
main_loss Primary loss function MSE, MAE
penalty_loss Secondary loss function MSE, MAE
loss_weights A list of weight for each loss [1, 0.1]
num_epoch Number of training epoch Integer: 1000
batch_size Batch size Integer: 55

Neural network

Key Definition Value
units A list of number of neurons per hidden layer Integer
act_funcs A list of activation function for each hidden layer relu, sigmoid, tanh


Key Definition Value
enable_gpu Train on GPU Logical: true, false
gpus Number of GPUs Integer


Key Definition Value
verbosity Level of output printing Integer: 1
show_summary Show DAENN summary Logical: true, false
save_tb Save TensorBoard file Logical: true, false
save_model Save trained model Logical: true, false
save_weights Save weights Logical: true, false
save_weights_npz Save weights in npz format Logical: true, false
save_biases_npz Save biases in npz format Logical: true, false
save_graph Save TensorFlow graph Logical: true, false
save_loss Save loss Logical: true, false
show_loss Show loss Logical: true, false
save_metrics Save metrics Logical: true, false
show_metrics Show metrics Logical: true, false


Key Definition Value
out_dir Path for output directory String
out_model Name of output model String
out_weights Name of output weights String
out_weights_npz Name of output weights in npz format String
out_biases_npz Name of output biases in npz format String
loss_plot Name of loss plot String
metrics_plot Name of metrics plot String